Hello!

Photo of (some of) the team :)
Photo of (some of) the team :)

This visual data story explores what drinks the customers order at my workplace! Because I am actually curious (hehe).

I have tried my best to collect data for every drink order received but because there are busy rushes and also my breaks, I wasn’t able to log/collect data on quite a few orders.

Overall drink orders from customers

The data has been observed and collected over four of the days I worked on 29th of March, 3rd of May, 4th of May, and 10th of May 2025.

Overall, the customers mostly order coffees and teas.

A horizontal bar chart of the total number of orders (observed) for each drink type. The bars for each drink type is coloured by a colour that reflects the drink e.g. bright olive green for matcha latte.
A horizontal bar chart of the total number of orders (observed) for each drink type. The bars for each drink type is coloured by a colour that reflects the drink e.g. bright olive green for matcha latte.

In this case, the total number of teas and coffees ordered by customers are the same.

Drink orders for each day the data was collected

An animation of the bar chart above. It cycles through a bar chart for each day the data was collected - 29 March, 3 May, 4 May, and 10 May. It shows how the total number of orders for each drink type differs (each day!).
An animation of the bar chart above. It cycles through a bar chart for each day the data was collected - 29 March, 3 May, 4 May, and 10 May. It shows how the total number of orders for each drink type differs (each day!).

But look! The total number of orders we get for each drink type are different for each day.

Remember how the total number of tea and coffee orders were the same in the overall plot? Well we can see that this isn’t the case when we look at the total number of orders for each day. Some days there are more tea orders and other days there are more coffee orders.

In conclusion, it differs!

(Also, pretend the x-axis scales are from 0 to 50. And 29 May says 29 March.)

Does the temperature outside affect whether customers get hot or iced drinks?

The majority of the orders were hot drinks and some of the orders were iced drinks.

A vertical bar chart of the number of orders (observed) for each temperature factor. It is categorised by whether the drink was hot or iced (i.e there is a bar chart for hot drinks and a bar chart for iced drinks).
A vertical bar chart of the number of orders (observed) for each temperature factor. It is categorised by whether the drink was hot or iced (i.e there is a bar chart for hot drinks and a bar chart for iced drinks).

The outside temperature doesn’t seem to affect whether the customers order hot drinks or iced drinks.

I mean that makes sense though! Most of the drinks we offer are better as a hot drink than as an iced drink (in my opinion haha).

Tea tea tea!

A (discrete, jittered) scatter plot with boxplots overlaid for each tea (type) category. It shows each individual tea order (as a point) with respect to the order time categorised by the tea type.
A (discrete, jittered) scatter plot with boxplots overlaid for each tea (type) category. It shows each individual tea order (as a point) with respect to the order time categorised by the tea type.

Was the plot a bit messy? Hard to understand?

Animated version of the plot above but without the boxplots overlaid.
Animated version of the plot above but without the boxplots overlaid.

I have animated the plot so that its easier to see!

It looks like the customers order any type of tea regardless of what time it is.

I expected the teas with the highest caffeine content to be more popular in the morning/early afternoon and the teas with the lowest caffeine content to be more popular in the afternoon - more popular implying more orders.

It is the case for traditional black teas as customers didn’t order any past 1PM. But for other tea types, I seem to have assumed wrong!

COFFEEEE!

A horizontal bar chart of the total number of orders (observed) for each coffee type + hot/iced chocolates.
A horizontal bar chart of the total number of orders (observed) for each coffee type + hot/iced chocolates.

As for the coffees, it looks like the most coffee orders we got were flat whites and the least coffee orders we got were short blacks.

This might be supporting the fact that the most popular coffee in New Zealand is flat white.

A “time plot” (a graph with points and a line connecting the points) counting the number of orders (observed) for each type of coffee + hot/iced chocolates with respect to the time (hour) of when it was ordered. There is a “time plot” for each type of coffee.
A “time plot” (a graph with points and a line connecting the points) counting the number of orders (observed) for each type of coffee + hot/iced chocolates with respect to the time (hour) of when it was ordered. There is a “time plot” for each type of coffee.

There isn’t much pattern to take away this “time” plot. It looks as though for some coffee types, there are more orders in the morning than in the afternoon.

Maybe if I collected more data and reliably logged all orders, then we might start to see some pattern in when customers order coffee for each coffee types?

The end!

Hope you enjoyed reading through my visual data story :D

As you can probably tell from the previous header, I love coffee and I’m a bit of a coffee snob so I’ll leave some pictures of my passion - making coffee and latte art!